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Phylogenomic Inference, Divergence-Time Calibration, and Methods for Characterizing Reticulate Evolution

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13 September 2023

Posted:

14 September 2023

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Abstract
Phylogenomics has enriched our understanding of the Tree of Life. Non-vertical modes of evolution—such as hybridization/introgression and horizontal gene transfer—deviate from a strictly bifurcating tree model, mirroring a network-like or reticulate structure. Here, we present an overview of a phylogenomic workflow for inferring organismal histories, calibrating those histories to evolutionary time, and detecting reticulate evolution. Mitigating analytical sources of error facilitates accurate reconstructions of evolutionary history and, in turn, characterization of non-vertical modes of evolution. Workflows and methods discussed herein may aid in the rigorous inference of organismal histories in geologic time and reticulation, providing a clearer understanding of the evolutionary process.
Keywords: 
phylogenetics
;  
hybridization
;  
introgression
;  
horizontal gene transfer
;  
lateral gene transfer
;  
phylogenetic incongruence
;  
gene-tree-species-tree discordance
Subject: 
Biology and Life Sciences  -   Ecology, Evolution, Behavior and Systematics

Introduction

Phylogenomics—phylogenetic analysis using genome-scale data—has been used to infer the evolutionary history of diverse lineages across the Tree of Life, including animals, fungi, plants, bacteria, archaea, and viruses (Dunn et al. 2008; Misof et al. 2014; Wickett et al. 2014; Worobey et al. 2016; Simion et al. 2017; Parks et al. 2018; Shen et al. 2018; One Thousand Plant Transcriptomes Initiative 2019; Zhu et al. 2019; Coleman et al. 2021; Galindo et al. 2021; Li et al. 2021; Tahon et al. 2021). These studies have resolved numerous phylogenetic controversies, deepening our understanding of life's history (Capella-Gutiérrez et al. 2012; King and Rokas 2017; Williams et al. 2019; Pipes et al. 2021; Steenwyk et al. 2023b). Phylogenomics has also proven useful for delineating lineage relationships at various taxonomic scales, ranging from species to higher-order relationships (Díaz-Tapia et al. 2017; Muñoz-Gómez et al. 2017; Mateo-Estrada et al. 2019; Bringloe et al. 2021; Steenwyk et al. 2022a; Sierra-Patev et al. 2023), and provides the necessary framework for comparative evolutionary genomic studies, such as determining gene duplication and loss events or studying phenotypic innovation (Zhang et al. 2014a; Steenwyk et al. 2019a; Fernández and Gabaldón 2020; Shen et al. 2020; Phillips et al. 2021; Li et al. 2022a; Opulente et al. 2023).
However, errors may be introduced during analytical steps, such as orthology and site-wise homology inference, resulting in inaccurate reconstructed evolutionary histories (Martín-Durán et al. 2017; Ashkenazy et al. 2019; Emms and Kelly 2019; Steenwyk et al. 2023b). Similarly, the inferred timing of ancestor-to-descendent divergence events can be erroneous due to misspecifying molecular clock models, fossil calibrations, or other factors (Ho and Duchêne 2014; Tao et al. 2020; Carruthers and Scotland 2021). Careful consideration of these analytical sources of error during experimental design is crucial for improving the accuracy of phylogenomic inference (Steenwyk et al. 2023b).
Incongruence between the evolutionary histories of single loci and organisms (locus-tree-species-tree incongruence or discordance) can also arise from biological factors (Steenwyk et al. 2023b). These factors include reticulate evolutionary processes like hybridization/introgression, the interbreeding between distinct lineages, which can disrupt inferences of both the timing and pattern of historical divergences (Rieseberg et al. 2007; Racimo et al. 2015; Barley et al. 2018; Gonçalves et al. 2018; Gonçalves and Gonçalves 2019; Steenwyk et al. 2020b; Li et al. 2022b; Suvorov et al. 2022; Tiley et al. 2023). Hybridization/introgression has been documented in plants, algae, fungi, and a variety of animals among other lineages (Rieseberg et al. 2007; Neafsey et al. 2010; Stukenbrock 2016; Edelman et al. 2019; Edger et al. 2019; Sousa et al. 2019; Mixão and Gabaldón 2020; Steenwyk et al. 2020b; Bringloe et al. 2021; Wang et al. 2022). Among humans, loci originating from admixture events between early humans and Neanderthals have been associated with adaptation, phenotypic variation, and disease risk, including for severe COVID-19 (Sankararaman et al. 2016; Simonti et al. 2016; Dannemann and Kelso 2017; Dannemann et al. 2017; Zeberg and Pääbo 2020). Hybridization can also result in allopolyploid wherein the genome of the hybrid organism encodes (nearly) the entire genome of both parents. Allopolyploidy has been observed in numerous plants, fungi, and a few vertebrates (Ozkan et al. 2001; Session et al. 2016; Edger et al. 2019; Steenwyk et al. 2020b; Chen et al. 2022; Session and Rokhsar 2023). Genome evolution in allopolyploids can be rapid, marked by pronounced loss of genetic material (Ozkan et al. 2001), or be relatively stable (Steenwyk et al. 2020b). In either case, introgression/hybridization results in novel combinations of genes and genetic backgrounds that can result in distinct phenotypic profiles (Steenwyk et al. 2020b; Bautista et al. 2021).
Another mode of reticulate evolution, horizontal gene transfer—the transfer of genetic material without sexual reproduction—also causes locus-tree-species-tree incongruence and has been documented in diverse organisms, especially among prokaryotes and archaea (Galtier 2007; Yue et al. 2012; Van Etten and Bhattacharya 2020; Arnold et al. 2022; Gonçalves and Gonçalves 2022; Gophna and Altman-Price 2022; Li et al. 2022b; Steenwyk et al. 2023a). Horizontal gene transfer can be advantageous, endowing recipient organisms with potentially novel functionality (Gonçalves and Gonçalves 2019; Kominek et al. 2019; Li et al. 2022b). In certain cases, complex patterns of horizontal gene transfer or lateral acquisition of entire gene clusters can occur, resulting in new metabolic capabilities such as alcohol fermentation and the biosynthesis of thiamine and siderophores (Gonçalves et al. 2018; Gonçalves and Gonçalves 2019; Kominek et al. 2019). Horizontally acquired genes can also facilitate adaptation to extreme environments. For example, ice-binding proteins originating from bacteria are thought to contribute to algal adaptation to Arctic environments (Dorrell et al. 2023) and mercuric reductase, an enzyme responsible for converting mercury to a less toxic form, was transferred from bacteria to extremophilic algae commonly isolated from environments with a high mercury concentration (Schönknecht et al. 2013). Among protists, approximately 1% of gene repertoires are estimated to have been horizontally acquired (Van Etten and Bhattacharya 2020). These observations emphasize the significance of horizontal gene transfer as a major evolutionary mode.
From data acquisition to divergence time estimation, this review outlines notable steps for phylogenomic inference, discusses potential analytical sources of error, and explores methodologies for detecting reticulate evolution. By rigorously considering sources of error, researchers can disentangle analytical and biological factors contributing to locus-tree-species-tree incongruence, thereby enhancing the accuracy of phylogenomic inference, and facilitating the characterization of reticulate evolutionary processes.

A Workflow for Robust Phylogenomic Inference

Data Acquisition and Preparation 

The first step of phylogenomic tree inference involves acquiring high-quality genomic/transcriptomic data from the target taxa (Figure 1A) (Cheon et al. 2020; Kapli et al. 2020). The abundance of available genomic/transcriptomic resources, accessible through online repositories like the National Center for Biotechnology Information (NCBI), enables researchers to address worthwhile questions without generating new data. Nevertheless, expanded taxon sampling through novel ‘omic resources can improve phylogenomic inference, help address contentious issues, and provide a valuable resource to the community (Pollock et al. 2002; Dunn et al. 2008; Wiens and Tiu 2012; Shen et al. 2018; Blaxter et al. 2022; Steenwyk et al. 2023b). To improve taxon sampling, researchers can apply a variety of methods to the taxa of interest, including targeted sequencing, reduced representation sequencing, transcriptomics, or whole-genome sequencing (Dunn et al. 2008; Peterson et al. 2012; One Thousand Plant Transcriptomes Initiative 2019; Hale et al. 2020). Applying standard methods for sequence data processing and quality control, such as removing low-quality data and contaminated sequences, is crucial (Zhou and Rokas 2014). Numerous tools exist for removing low-quality sequencing data, such as Trimmomatic and fastp (Bolger et al. 2014; Chen et al. 2018). Contaminated sequences can be purged from datasets using dinucleotide odds ratios or coverage-versus-lengths plots (Schmieder and Edwards 2011; Douglass et al. 2019).
For the sake of simplicity, the following sections of this review focus on phylogenomic inference using alignments of protein-coding sequences. However, it is important to note that different data sources require tailored approaches to facilitate the subsequent steps. For instance, when working with genomic data, an additional step of gene boundary predictions is necessary (Stanke et al. 2006; Brůna et al. 2021). Similarly, SNP-based phylogenomics, which is also suitable for detecting hybridization/introgression, or synteny data, an emerging phylogenomic marker of collinearity between two or more genomes (Rokas and Holland 2000; Bringloe et al. 2021; Parey et al. 2023; Schultz et al. 2023; Steenwyk and King 2023), requires unique considerations not covered here.

Gene Orthology Determination 

Genes that arise from speciation events in a shared ancestor are termed orthologous genes and serve as the foundation for phylogenomic analysis (Gabaldón and Koonin 2013). Relationships among orthologous genes can be described as one of three categories: one-to-one, one-to-many, and many-to-many (Fernández et al. 2019). Considering two genomes, one-to-one orthologs are encoded in each genome once; one-to-many orthologs are encoded in one genome once and the other multiple times; and many-to-many orthologs refer to a gene with multiple copies in each genome. Although methods have been developed to infer organismal histories using one-to-many and many-to-many orthologs (Zhang et al. 2020; Smith et al. 2022), most phylogenomic analyses rely on one-to-one orthologous genes and will be the focus of the present article. Moreover, single-copy orthologs are often the substrate of many downstream molecular evolution analyses such as measures of selection, relative evolutionary rates, and gene-gene coevolution (Chikina et al. 2016; Kowalczyk et al. 2019; Steenwyk et al. 2021, 2022d; Álvarez-Carretero et al. 2023).
Inferring orthology can be accomplished through two main approaches: global and targeted inference. A commonly utilized framework for global orthology inference involves all-to-all sequence similarity calculations followed by clustering (Figure 1B). Several tools implement this approach, including OrthoFinder, OrthoMCL, or MMseqs2 (Li et al. 2003; Emms and Kelly 2015, 2019; Steinegger and Söding 2018). To perform all-to-all sequence similarity calculations, software like BLAST, DIAMOND, or MMseqs2 can be employed (Camacho et al. 2009; Steinegger and Söding 2018; Buchfink et al. 2023). Sequence similarity scores can be influenced by biases arising from differences in sequence lengths. Correcting this analytical error can enhance the accuracy of orthology inference (Emms and Kelly 2015). Subsequently, graph-based clustering methods, such as the Markov Cluster Process (Van Dongen 2008), are applied to categorize genes into distinct orthologous groups of genes. During Markov clustering, the inflation value will impact the granularity of orthologous groups of genes (Brohée and Van Helden 2006). Relaxed inflation values may combine orthologous groups of genes whereas stringent inflation values may oversplit them. Oversplitting can also stem from the inability to detect remote homology among orthologs encoded in distantly related species or rapidly evolving taxa (Weisman et al. 2020). There is no golden rule for an appropriate inflation value; the best value will likely be dataset-dependent. The resulting clusters of orthologous genes can serve as proxies for gene families.
In phylogenomic inference, single-copy gene families, orthologous genes encoded in each taxon once, are preferred as they (presumably) have not experienced duplication or loss (Li et al. 2017). Organismal histories are often inferred using hundreds to thousands of loci to avoid inadequate sampling of gene loci, a known source of error while inferring organismal histories (Rokas et al. 2003; Steenwyk et al. 2023b). However, as the number of taxa and evolutionary distances between them increases, the number of single-copy orthologous genes tends to decrease (Emms and Kelly 2018). For instance, in a dataset comprising 42 plants with complex evolutionary histories involving dynamic gene gain and loss events (Clark and Donoghue 2018), no single-copy orthologous genes were identified (Emms and Kelly 2018). In such cases, no gene families are fit for standard phylogenomic analyses.
To overcome this challenge, tree decomposition algorithms that partition multi-copy gene families into subgroups of single-copy orthologous genes and pruning algorithms that remove species-specific paralogs (i.e., duplicated genes observed in a single species) can be useful (Kocot et al. 2013; Willson et al. 2022). The software OrthoSNAP combines tree splitting and pruning to identify single-copy orthologs nested within larger gene families (Steenwyk et al. 2022c). This process is analogous to snapping branches in a tree; thus, the resulting subgroups of single-copy orthologs are termed SNAP-OGs (splitting and pruning). Single-copy gene families and SNAP-OGs are statistically indistinguishable across diverse measures of information content (Steenwyk et al. 2022c), indicating their suitability for phylogenomic analyses.
While global orthology inference offers valuable insights, it often demands significant computational resources. An alternative approach involves the identification of pre-determined single-copy genes (Figure 1B). One method is the BUSCO pipeline (Benchmarking Universal Single-Copy Ortholog), which identifies near-universally single-copy orthologous genes within a genome or transcriptome (Waterhouse et al. 2018). BUSCO uses predefined near-universally single-copy orthologs from OrthoDB (Kriventseva et al. 2019). This analysis also serves as a useful quality control measure for assessing assembly gene content completeness (Waterhouse et al. 2018). When researchers prefer to use a custom set of predefined single-copy orthologous genes, orthofisher can be used (Steenwyk and Rokas 2021). Additionally, various sequence similarity search tools such as HMMER, BLAST, and DIAMOND can be employed to support the identification of single-copy orthologous genes (Camacho et al. 2009; Eddy 2011; Buchfink et al. 2023). If the putative orthologs identified using this approach are multi-copy, the same splitting and pruning procedure in OrthoSNAP can identify SNAP-OGs (Steenwyk et al. 2022c). If the dataset contains a small number of genes, single-locus phylogenies can also be visually inspected, and paralogous sequences can be manually removed.

Multiple Sequence Alignment and Trimming 

Once a curated set of phylogenomic markers has been obtained, the next step is multiple sequence alignment (Figure 1C), which aims to determine the site-wise homology across a group of sequences. Progressive alignment is a widely used strategy for multiple sequence alignment and involves an iterative pairwise alignment approach (Feng and Doolittle 1987). Over time, advancements have incorporated additional biological information to improve multiple sequence alignment. For instance, PRANK utilizes a phylogeny-aware approach, while 3DCoffee incorporates protein structure information (O’Sullivan et al. 2004; Löytynoja and Goldman 2005).
Several databases, such as BAliBASE and PREFAB (Thompson et al. 1999; Edgar 2004), have been developed to evaluate the effectiveness of these approaches. By using gold standard alignments from such databases or alignments generated by simulation software like INDELible (Fletcher and Yang 2009), the accuracy of multiple sequence alignment algorithms can be assessed using metrics like sum-of-pairs (pairwise alignment accuracy) and column scores (column-wise alignment accuracy) (Thompson et al. 1999; Steenwyk et al. 2021). Additionally, secondary structure predictions derived from the aligned sequences can be compared to known secondary structures to evaluate alignment accuracy (Sievers and Higgins 2020). Despite the establishment of standards, variable outcomes among benchmarking studies indicate that there is currently no universally superior algorithm for multiple sequence alignment (Raghava et al. 2003; Wang et al. 2018b; Sievers and Higgins 2020). Generating study-specific simulated alignments based on expected phylogenetic diversity can help determine which alignment strategy performs well for a given dataset.
Multiple sequence alignments are commonly subjected to trimming, which involves the removal of specific sites or blocks of sites within alignments. Traditionally, sites are trimmed because they are highly variable, which is thought to stem from erroneously inferred site homology, or are devoid of phylogenetic informativeness due to saturation by multiple substitutions (Talavera and Castresana 2007; Criscuolo and Gribaldo 2010). However, comparing multiple sequence alignment trimming algorithms has revealed that these methods often lead to poorer phylogenetic inference for single-gene analyses (Tan et al. 2015). This finding suggests that current trimming methods may inadvertently remove phylogenetically informative sites. In contrast, the software ClipKIT uses an alternative approach where informative sites (e.g., parsimony informative sites) are retained and all others removed, outperforming other algorithms under diverse evolutionary scenarios (Steenwyk et al. 2020a). A secondary advantage of trimming multiple sequence alignments is that shorter alignments require fewer computational resources, an important consideration for environmentally conscious computing practices (Kumar 2022; Steenwyk et al. 2023b). To address other types of errors in multiple sequence alignments, tools such as TAPER, Divviers, or MACSE can be employed (Ranwez et al. 2018; Ali et al. 2019; Zhang et al. 2021).

Model Selection 

In molecular phylogenetics, the identification of an optimal model of sequence evolution occurs after alignment trimming and before tree inference. Although substitution models may, at times, not have a large impact on phylogenetic inference, model misspecification is a well-appreciated source of error (Shen et al. 2018; Abadi et al. 2019; Betancur-R. et al. 2019; Cao et al. 2022; Steenwyk et al. 2023b). Accordingly, substitution models have been a major research focus. Two main categories of substitution models exist: site homogeneous and site heterogeneous. Site homogeneous models employ the same parameters of character frequencies and substitution rates across the entire alignment (Galtier and Gouy 1995). Site heterogeneous mixture models allow for different character frequencies at each site (Lartillot et al. 2007; Si Quang et al. 2008). Site homogeneous models may be more prone to model misspecification, which can be overcome by generating alignment-specific time-reversible and time-non-reversible substitution models (Rodríguez-Ezpeleta et al. 2007; Minh et al. 2021). Site heterogeneous models have a larger parameter space and are robust to over-parameterization but require substantial computational resources (Baños et al. 2022). To accelerate computation, site heterogeneous models can be approximated using posterior mean site frequency profiles (Wang et al. 2018a).
Software tools that perform model testing are often integrated into phylogenetic tree inference software. For example, ModelFinder is included in the IQ-TREE toolkit (Kalyaanamoorthy et al. 2017; Minh et al. 2020b). These tools employ statistical frameworks—such as likelihood ratio testing and calculations of information criterion—to facilitate assessing model fit (Darriba et al. 2012, 2020; Kalyaanamoorthy et al. 2017).

Gene Alignment Concatenation or Coalescence-based Methods for Inferring Organismal History 

There are two commonly used approaches for organismal tree inference from genome-scale datasets: gene alignment concatenation (or simply concatenation) and coalescence (Figure 1D) (Rokas et al. 2003; Liu et al. 2009a; Steenwyk et al. 2023b). Each employs different theoretical and statistical frameworks. Concatenation primarily utilizes maximum likelihood or Bayesian statistics. Coalescence relies on the multi-species coalescent model, which accounts for discordance between single loci and species-trees stemming from processes like incomplete lineage sorting.
The concatenation approach combines the aligned and trimmed orthologous genes into a single matrix. SequenceMatrix, FASconCAT-G, and PhyKIT can concatenate multiple sequence alignments (Vaidya et al. 2011; Kück and Longo 2014; Steenwyk et al. 2021). The resulting supermatrix can then be analyzed using various software that employs maximum likelihood statistical frameworks, such as RAxML-NG and IQ-TREE (Kozlov et al. 2019; Minh et al. 2020b), or Bayesian frameworks, such as MrBayes, BEAST, PhyloBayes, and RevBayes (Huelsenbeck and Ronquist 2001; Ronquist et al. 2012; Höhna et al. 2016; Bouckaert et al. 2019; Lartillot 2020). The RevBayes software allows users to customize how parameters, priors, and data are directly connected in the model, either in the Rev language or in the R programming language (Höhna et al. 2017; Charpentier and Wright 2022). In concatenation approaches, a single substitution model can be applied to the entire alignment, or the supermatrix can be partitioned, with different substitution models applied to each partition (Kainer and Lanfear 2015).
Comparative studies have systematically evaluated the performance of tools implementing different flavors of heuristics for maximum likelihood calculations and tree rearrangement methods, revealing variation in their performance (Zhou et al. 2018). This observation suggests that software choice is an important consideration. More broadly, software choice is encompassed in a newly described source of error: treatment errors, the negative impact of software choice, or data treatment upon stochastic and systematic errors (Steenwyk et al. 2023b).
In coalescent-based methods, two different approaches—one-step and two-step methods—are commonly employed for phylogenomic inference. In one-step approaches, single-locus phylogenies are estimated simultaneously with the species tree. This methodology is implemented in software such as BEST, StarBeast, and bpp (Liu et al. 2008; Yang and Rannala 2010; Douglas et al. 2022). Similarly, SVDquartets derives species trees from aligned, unlinked single nucleotide polymorphism data using the coalescent framework (Chifman and Kubatko 2014). In two-step approaches, as implemented in STAR and ASTRAL (Liu et al. 2009b; Zhang et al. 2018), involve first inferring individual single-locus phylogenies and then constructing a summary tree from the collection of single-locus phylogenies. One-step approaches are computationally expensive and may be difficult to apply to large phylogenomic datasets. Two-step approaches may be susceptible to errors resulting from inaccurate single-locus tree inference (Degnan and Rosenberg 2009). Collapsing poorly supported branches before applying summary tree methods can help mitigate the impact of uncertainty in single-locus phylogenies (Steenwyk et al. 2023b).
Although both concatenation and coalescence methods are widely used in species tree inference, there is currently no definitive guidance on when to prefer one method. However, it has been consistently observed that concatenation and coalescence can yield different topologies and varying levels of gene-wise support (Gatesy et al. 2017; Shen et al. 2018, 2021; Steenwyk et al. 2019b, 2023b; Li et al. 2020). The performance of these methods may vary depending on factors such as the data characteristics—the number and length of sampled loci, completeness of taxon sampling, the extent of reticulate evolution or incomplete lineage sorting among taxa, and the evolutionary diversity of the taxa, for example. A comprehensive study is needed to elucidate evolutionary scenarios where one method outperforms the other.

Examining Bipartition Support 

Assessing the confidence in the resulting phylogenetic tree is crucial for identifying unstable bipartitions and gaining insights into lineages with reticulate evolutionary histories (Steenwyk et al. 2023b). Various methods have been developed to evaluate the support for different branches in a phylogeny and identify poorly supported bipartitions.
Bootstrapping is one of the earliest and most widely used methods for evaluating confidence in phylogenetic tree inference (Figure 1E). This statistical procedure involves resampling sites from an alignment with replacement to create multiple replicates of the original data (Felsenstein 1985). Each replicate is then used to infer a phylogenetic tree, and the frequency of observing a particular branch among the bootstrap replicates quantifies the support for that clade. Bootstrapping is typically performed with many replicates, such as 100 or more, to obtain robust support values. However, bootstrapping can be computationally intensive and time-consuming.
As phylogenomic datasets have become larger, alternatives have been introduced to expedite computation. For example, RAxML implements a rapid bootstrap approach (Stamatakis et al. 2008) and IQ-TREE employs an ultrafast bootstrap approximation method (Hoang et al. 2018). The "bag of little bootstraps" can also reduce the computational burden of long alignments by combining resampling with subsampling (Sharma and Kumar 2021). The gradual "transfer" distance method has also been proposed for bootstrap inference in phylogenies with hundreds to thousands of taxa (Lemoine et al. 2018).
Other methods to evaluate confidence use branching patterns observed among single-gene phylogenies. These include single-locus or -site support frequencies (also known as concordance factors) and quartet mapping (Figure 1E), as implemented in IQ-TREE, ASTRAL, and PhyKIT (Zhang et al. 2018; Minh et al. 2020b; Steenwyk et al. 2021). Calculations of internode certainty, an entropy-based measure of branch support, can help identify when alternative topologies are well supported among a collection of single-locus or bootstrap trees (Salichos and Rokas 2013; Salichos et al. 2014; Kobert et al. 2016; Zhou et al. 2020).
Phylogenomic subsampling is another approach for exploring tree space and identifying unstable bipartitions (Figure 1E) (Edwards 2016; Steenwyk et al. 2023b). In phylogenomic subsampling, subsamples of loci in a full phylogenomic data matrix are used to re-infer organismal histories. Bipartitions incongruent between the full and subsampled matrices warrant further investigation and can be considered unstable. Subsampling is typically done by selecting, for example, half of the loci in a full data matrix with the desirable feature associated with phylogenetic signal. These features—such as alignment length, average bipartition support, treeness divided by relative composition variability, and the number of parsimony and variable sites, among others (Phillips and Penny 2003; Shen et al. 2016, 2018; Steenwyk et al. 2019b, 2020a, 2021, 2022b; Mongiardino Koch 2021; McCarthy et al. 2023; Redmond et al. 2023)—aim to capture the information content of a locus.

Time-calibration of inferred phylogenetic divergences 

Divergence times among branches in a phylogenomic analysis can be estimated by using fossils, mutation rates, or other temporal evidence to calibrate a molecular clock model (Ho and Phillips 2009; Dos Reis et al. 2016, 2018; Tiley et al. 2020). This procedure converts the relative divergences of molecular substitution rates to absolute time, often in units of thousands or millions of years ago. The resulting time-calibrated phylogenies, which are often referred to as ‘timetrees’ or ‘chronograms,’ differ from uncalibrated phylogenies (‘phylograms’) in that the former is directly comparable to other types of time-scaled data. Timetrees can be used to investigate causal eco-evolutionary dynamics relative to a broad array of independent evidence; for example, past changes in global temperature versus rates of lineage divergence (Oliveros et al. 2019; Schubert et al. 2019; S. Meseguer and Condamine 2020; Feijó et al. 2022); co-diversification among taxa (Sabrina Pankey et al. 2022; Nelsen et al. 2023); and rates of speciation in related clades (Harvey et al. 2020; Upham et al. 2021).
Approaches to estimating divergence times can be divided into node dating, tip dating, and fossil-free dating. Node dating places temporal constraints (i.e., calibrations) on a bifurcating internal node of a phylogeny, whereas tip dating places calibrations on terminal taxa that existed at some time in the past (Ho and Phillips 2009; Heath et al. 2014). The ages of serially sampled taxa—usually fossils or viruses and other microbes (Stadler and Yang 2013)—are the most reliable data for calibrating divergence times in phylogenomic datasets. Fossils and their associated ages (typically a confidence interval for the age of rock formations above and below a fossil discovery) can calibrate divergence times at either nodes or tips, typically using a probability distribution to incorporate age uncertainty (Ho and Phillips 2009; Stadler and Yang 2013). A fossil's phylogenetic position relative to living members of a given clade must be inferred or assumed based on other data for that fossil to serve as a time calibration (Parham et al. 2012). Viruses and other microbes evolve rapidly enough that samples collected in the last few decades offer valuable tip calibrations, analogous to the role of fossils in longer-lived mammals or plants (Volz et al. 2013; Andréoletti et al. 2022). The resulting ‘phylodynamic’ analyses help expose the population-dynamic processes that generate the phylogenetic patterns inferred from phylogenomic datasets. Phylodynamics is a promising integration of phylo-centric fields, enabling advances in cell biology, epidemiology, and macroevolution (Stadler et al. 2021; Andréoletti et al. 2022).
Clock models are used to extrapolate species divergence times from calibrated nodes. Strict clock models assume a fixed mutation rate in all branches, which is often violated when comparing more distant relatives (e.g., the 2% per million years rate used for bird mitochondrial genes; (Ho 2007)). However, strict clocks may lack biological realism. To address this, the strict clock assumption can be relaxed, such as in autocorrelated clock models wherein closely related branches have similar mutation rates or, in uncorrelated models, each branch is given an independent rate (Drummond et al. 2006; Lepage et al. 2007; Steenwyk and Rokas 2023). Relaxed clocks allow greater flexibility for handling the inherent molecular-rate variation among lineages, and thus are in wide use today for all types of time-calibration approaches, including fossil dating and multi-species coalescent approaches (Dos Reis et al. 2016, 2018; Flouri et al. 2022). These latter approaches enable the simultaneous estimation of species divergence times and ancestral population sizes using phylogenomic data and can be quite accurate when mutation rates are known from pedigrees (Tiley et al. 2020).
What if no fossils or other serial samples are available for a particular taxon? Two main options exist to calibrate divergences: use a fixed, strict clock model to project estimates back from tips or secondary calibrations. Secondary calibrations use previously estimated (from primary fossil or rate calibrations) divergence times of a sister taxon or outgroup to calibrate an internal or root node in the clade of interest (Shaul and Graur 2002). However, caution is required to avoid specifying overly precise secondary calibrations (Schenk 2016).
Choosing which software to use for divergence-time estimation involves a trade-off between available compute resources and the desired level of biological realism. At one extreme, the most realistic models (e.g., BPP and StarBEAST (Flouri et al. 2018; Douglas et al. 2022)) will perform Bayesian inference to estimate multi-species coalescent parameters across thousands of gene genealogies, considering multiple rate priors, and integrating across both phylogenetic and temporal uncertainty to yield a posterior distribution of time-scaled trees. However, these ‘full methods’ do not scale to large numbers of taxa or distant relatives (Tiley et al. 2020; Jiao et al. 2021).
At the other extreme, concatenated sequence data are used step-wise to first estimate the phylogenetic tree topology in units of substitutions/site, and then calibrations are applied in a second step of divergence-time estimation. Step-wise methods most commonly use maximum-likelihood (e.g., r8s, treePL, RelTime; (Sanderson 2003; Smith and O’Meara 2012; Tao et al. 2020)), but can also be implemented using Bayesian inference in programs like BEAST or MrBayes, which often requires fixing the tree topology. Midway between these extremes is the use of concatenated sequence data to perform simultaneous estimation of topology and divergence times, generally as implemented in a Bayesian framework (e.g., BEAST, MCMCtree, MrBayes, PhyloBayes, RevBayes). This latter approach has been implemented in large datasets (e.g., 800 taxa by 40,000 sites; (Upham et al. 2019)), and continues to be aided by GPU-based computing libraries (Ayres et al. 2019). Strategies for setting priors can impact divergence time estimation and are an important consideration (Barba-Montoya et al. 2017).
During divergence time estimation, a range of dates are typically plausible under the experimental conditions. Thus, divergence times are depicted as a confidence interval. Divergence times can also be inferred using a bootstrapping approach for intractably large datasets. Overall, the choices of node, tip, or fossil-free dating; strict or relaxed clocks; and concatenation or multi-species coalescent methods depend upon the question of interest, available molecular and morphological data, and prevalence of locus-tree-species-tree incongruence.

Detecting Reticulate Evolution 

Reticulate evolutionary processes, such as hybridization/introgression and horizontal gene transfer, result in loci with evolutionary histories that differ from organismal history (Dobzhansky 1982; Abbott et al. 2013; Steenwyk et al. 2023b). There is a spectrum of outcomes for hybridization ranging from adaptive changes due to ecological selection, or compromised viability or fertility due to hybrid incompatibilities (Racimo et al. 2015; Moran et al. 2021). Similarly, horizontal gene transfer endows recipient organisms with novel genetic material and can be adaptive or not (Schönknecht et al. 2013; Gonçalves and Gonçalves 2019; Arnold et al. 2022; Gophna and Altman-Price 2022; Li et al. 2022b; Dorrell et al. 2023). The impact of horizontal gene transfer is well appreciated among prokaryotes and archaea but also occurs in the eukaryotes (Shen et al. 2018; Gonçalves and Gonçalves 2019; Lartillot 2020; Arnold et al. 2022; Gophna and Altman-Price 2022; Li et al. 2022b).

Hybridization/introgression 

Hybridization/introgression can be adaptive or not, either expanding the ecological repertoire of organisms as exemplified by sunflower adaptation to novel environments or leading to the reabsorption of incipient species (Mallet 2005, 2008; Racimo et al. 2015; Buck et al. 2023). Hybrid progeny can have improved growth and reproductive success or conversely be sterile (Zanewich et al. 2018; Qiao et al. 2019; Allen et al. 2020; Adavoudi and Pilot 2021). Hybridization has been observed in microbial pathogens and thus may contribute to higher or lower organismal virulence (Lin et al. 2009; Depotter et al. 2016; Mixão and Gabaldón 2020). Hybridization can also result in neutral or negative fitness outcomes (Allen et al. 2020; Adavoudi and Pilot 2021).
The identification of hybridization/introgression events can be accomplished by utilizing phylogenetic and comparative genomic methodologies (Scannell et al. 2006; Marcet-Houben and Gabaldón 2015; Ortiz-Merino et al. 2017; Mixão and Gabaldón 2020; Steenwyk et al. 2020b, 2023a). Among phylogenetic approaches, it is crucial to discriminate between incongruences among single-locus phylogenies stemming from hybridization instead of incomplete lineage sorting, the random sorting of ancestral alleles that can, at times, result in locus-tree-species-tree incongruence (Yu et al. 2013). To make this distinction, two nearly equally supported topologies among multiple single-locus phylogenies indicate hybridization, especially if hybridization was a recent event. Conversely, in the case of incomplete lineage sorting, the alternative topology is expected to be observed less frequently, especially among more recent divergences (Steenwyk et al. 2019b). The expected degree of incongruence stemming from incomplete lineage sorting can be modeled using the multispecies coalescent model. Deviations from that model may indicate a hybridization event (Degnan and Rosenberg 2009). Calculating internode certainty can also help quantify the degree of support for the two most prevalent topologies (Salichos and Rokas 2013; Kobert et al. 2016).
Many methods have been developed to detect hybridization events more directly using phylogenetic trees (Hibbins and Hahn 2022). One pioneering approach is the utilization of the D-statistic, also known as the ABBA-BABA test, which employs biallelic site patterns within a phylogenetic framework (Figure 2) (Green et al. 2010). Specifically, the ABBA-BABA test examines asymmetric support between ABBA and BABA patterns at biallelic sites, which suggests an introgression/hybridization event; in contrast, equal proportions of ABBA and BABA site patterns would suggest no introgression/hybridization. Leveraging genome-scale data, the ABBA-BABA test accurately quantifies introgression across a wide parameter space (Zheng and Janke 2018).
Discerning the direction of introgression poses a greater challenge (Martin et al. 2015). To address this, the Dp-statistic, an extension of the ABBA-BABA test, incorporates patterns observed across all variable sites, thus enabling the determination of gene flow directionality (Hamlin et al. 2020). In the context of a symmetric five-taxon phylogeny, the DFOIL statistic, another extension of the ABBA-BABA test built on the partitioned D-statistic (Eaton and Ree 2013), explicitly examines all potential introgression events, facilitating precise and sensitive detection of the extent and direction of introgression (Pease and Hahn 2015). Several other methodologies, including the F4-ratio, fd, the f3-statistic, and algorithms implemented in the HyDe software, have been developed to identify introgression events (Green et al. 2010; Reich et al. 2012; Martin et al. 2015; Blischak et al. 2018; Jacobs et al. 2018). Note, these methods examine rates of tree incongruences and are distinct from F-statistics, such as Fst, which are related to population genetic analysis.
In the context of allopolyploid hybrids, where the genome of the hybrid organism contains (nearly) the complete genetic complement of both parental genomes and, therefore, two or more copies of most genes, supplementary methods play a crucial role in detecting and unraveling the evolution of a hybrid genome. Ancient allopolyploid events can be identified by a burst of gene duplications (Chain et al. 2011; Marcet-Houben and Gabaldón 2015; Session et al. 2016), but would benefit from other lines of evidence such as synteny information. In allopolyploids, it may be helpful to determine the parent of origin for each gene in a hybrid genome. This can be accomplished by evaluating phylogenetic distances between the parental species and the gene copies encoded in the hybrid (Steenwyk et al. 2023a). Additionally, Ks plots, which visualize sequence divergence between loci in the hybrid taxon and their closest homolog in a parental species, prove valuable in assigning parent-of-origin for loci within a hybrid genome (Ortiz-Merino et al. 2017; Steenwyk et al. 2020b).

Horizontal gene transfer 

Horizontal gene transfer, or lateral gene transfer, is the acquisition of genetic material from other organisms without sexual reproduction (Mallet 2005). Horizontal gene transfer is considered a major mode of evolution in prokaryotes and archaea wherein the mechanism of transfer—conjugation, transduction, and transformation—is better understood (Galtier 2007; Arnold et al. 2022; Gonçalves and Gonçalves 2022; Gophna and Altman-Price 2022). In recent years, horizontal gene transfer in eukaryotes is better appreciated (Coelho et al. 2013; Gonçalves et al. 2018; Husnik and McCutcheon 2018; Shen et al. 2018; Zhou et al. 2018; Gonçalves and Gonçalves 2019; Van Etten and Bhattacharya 2020; Irwin et al. 2021). One hypothesized transfer mechanism among microeukaryotes serves as the foundation for the “you are what you eat” hypothesis, which posits that organisms that phagocytose prey may be more susceptible to rare and accidental nucleic acid integration from prey DNA (Sibbald et al. 2020). In support of this hypothesis, recent analyses found that carnivorous mammal genomes contain a greater prevalence of DNA-based transposable elements than do herbivores or omnivores, suggestive of origins via horizontal gene transfer from ingested prey or their viruses (Osmanski et al. 2023).
Horizontal gene transfer is also well appreciated in the context of mitochondrial and plastid organellogenesis. These organelles arose from the ancient assimilation and degradation of endosymbiotic bacteria in eukaryotic cells, during which many endosymbiont genes were lost or transferred to the host nucleus (Cenci et al. 2018; Ponce-Toledo et al. 2018; Sibbald and Archibald 2020). Charting endosymbiotic transfers has aided in estimating the order and mechanisms of higher-order plastid acquisitions and revealed that many plastid-related genes have highly chimeric origins (Stiller et al. 2014; Dorrell et al. 2017; Strassert et al. 2021). These observations may support scenarios of ancient serial plastid replacements before permanent plastid integration (Minge et al. 2010; Ponce-Toledo et al. 2018; Morozov and Galachyants 2019). Transferred genes have also provided evidence of relic plastids, illuminating patterns of plastid gain and loss (Cenci et al. 2018; Gawryluk et al. 2019). Some barriers to horizontal gene transfer are also known, such as multicellularity and incompatible genetic codes (Yue et al. 2012; Shen et al. 2018).
The methods employed for detecting horizontally acquired loci vary in precision and accuracy. Early methods relied on identifying deviations in gene sequence characteristics. In the case of very recent prokaryote-to-eukaryote horizontal gene transfer, detection could be achieved by observing genes that deviate in guanine-cytosine content, intron content, gene order, and codon usage across the host genome (Friedman and Ely 2012; Zhang et al. 2014b; Jaramillo et al. 2015; Gonçalves and Gonçalves 2022). In the phylogenomic era, these methods are often employed to support the identification of horizontal gene transfer events rather than serving as primary detection tools.
Another approach is to calculate the alien index—a score that compares the similarity between sequences within the target group and sequences from outgroup taxa (Gladyshev et al. 2008; Alexander et al. 2016)—of all genes in a host genome. Loci exhibiting alien indices indicative of potential horizontal gene transfer are then selected for further investigation through phylogenetic inference, the gold standard approach for horizontal gene transfer detection. Several software tools have been developed to calculate alien indices or similar metrics for assessing horizontal gene transfer. Examples include AvP, HGTector, and HGTphyloDetect (Zhu et al. 2014; Koutsovoulos et al. 2022; Yuan et al. 2023).
Phylogenetic trees that suggest horizontal gene transfer events are characterized by the confident placement of one or a few sequences within an unexpected taxonomic group (Figure 3). For instance, in the case of prokaryote-to-eukaryote horizontal gene transfer, sequences in a eukaryotic genome may be nested deep within a prokaryotic lineage (Coelho et al. 2013; Gonçalves et al. 2018; Husnik and McCutcheon 2018; Shen et al. 2018; Zhou et al. 2018; Gonçalves and Gonçalves 2019; Kominek et al. 2019; Van Etten and Bhattacharya 2020; Irwin et al. 2021; Li et al. 2022b). The evidence for horizontal gene transfer can be strengthened using topology tests, such as the Kishino-Hasegawa and Shimodaira-Hasegawa tests (Kishino and Hasegawa 1989; Shimodaira and Hasegawa 1999). These tests compare the likelihood of a phylogeny constrained to reflect a vertical evolutionary scenario (the null hypothesis) with the observed topology, reflecting the occurrence of horizontal gene transfer (the alternative hypothesis) (Gonçalves et al. 2018; Shen et al. 2018).
Although putatively transferred genes should be investigated using molecular phylogenetics, accurate phylogenomic species tree inference may not be crucial since it is widely accepted that the taxa involved are of distinct lineages; for example, fungi and bacteria are well known to comprise different clades. However, when examining horizontal gene transfer among more closely related taxa, robust species tree inference becomes essential (Ropars et al. 2015). The potential for shared ancestral variation or asymmetric gene loss patterns among closely related organisms increases the risk of erroneously inferring horizontal gene transfer events (i.e., type I error / false positive). Horizontally transferred loci may also complicate accurate inference of organismal histories; thus, purging phylogenomic data matrices from potentially horizontally acquired loci may be warranted. Lastly, to rule out putatively horizontally transferred genes stemming from contamination, this analysis is often restricted to well-assembled contigs, devoid of contamination signatures (Shen et al. 2018; Li et al. 2022b).

Phylogenetic Networks 

Hybridization/introgression and horizontal gene transfer challenge the strictly bifurcating tree model. Phylogenetic networks can help explore organismal histories in the presence of these factors (Huson 1998; Lutteropp et al. 2022; Steenwyk et al. 2023b), and to large extent can be viewed as complementary to strictly bifurcating phylogenetic analyses (Blair and Ané 2020). Phylogenetic networks can be broadly categorized into reticulate networks, which capture reticulate evolutionary processes (Huson et al. 2005), and splits networks, which represent all splits in a collection of single-locus phylogenies (Huson 1998). Additionally, visualizing the density of phylogenetic trees can reveal common branching patterns across these collections, which can help identify regions of the tree that require further investigation. DensiTree is a tool commonly employed to visualize the density of phylogenetic tree topologies in a posterior distribution of trees (Bouckaert 2010). Gene-support frequencies (or quartet frequencies) and gene-wise phylogenetic signals offer additional insights to scrutinize and assess alternative branching patterns (Shen et al. 2017, 2021; Sayyari and Mirarab 2018; Steenwyk et al. 2019b; Minh et al. 2020a). The methods discussed herein facilitate rigorous phylogenomic inference and detection of reticulate evolution in organismal histories.

Conclusion 

Knowledge of organismal history underpins many evolutionary studies. This article outlines the many branching choices that investigators must make when performing phylogenomic inference, and some of the known pathways toward assembling robust methodological pipelines. The resulting analyses of genome-wide evolutionary signatures can, in turn, be used to enrich our understanding of life's history and the evolutionary process. Ameliorating analytical factors driving apparent incongruence will facilitate more accurate inference of organismal histories. True histories of reticulate evolution will deepen our understanding of the Tree of Life, adding a dimension of ‘webiness.’ Future computational improvements, reduced cost of high-quality genome sequencing, and algorithmic advances culminating in more chromosome-scale genome assemblies will pave the way for increasing larger datasets and greater elucidation of the Tree of Life. Such advances will also bring new challenges and opportunities. To encourage the continued investigation of biodiversity via phylogenomics, we hope this article provides helpful guidance for scientific investigation and pedagogy alike.

Funding

J.L.S. is a Howard Hughes Medical Institute Awardee of the Life Sciences Research Foundation. N.S.U. was supported by Arizona State University start-up funds. H.V. received support from the Australian Biological Resources Study (4-G046WSD) and the Australian Research Council (DP200101613).

Acknowledgments

We thank Drs. Xing-Xing Shen and Yuanning Li for helpful discussion over the years. J.L.S. thanks Dr. Antonis Rokas, who taught him to embrace phylogenomic incongruence. Chat-GPT was used to edit portions of the manuscript; AI-generated text was further edited.

Competing Interests

J.L.S. is a scientific advisor for WittGen Biotechnologies. J.L.S. is an advisor for ForensisGroup Inc.

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Figure 1. A workflow for phylogenomic inference. (A) The first step in a phylogenomic workflow is data acquisition and preparation. This often entails identifying gene boundaries in genome assemblies or assembling transcripts in transcriptomes. (B) The next step is to identify orthologs using (left) de novo approaches—for example, all-by-all sequence similarity calculations followed by graph-based clustering—or (right) from predetermined sets of orthologs. (C) Orthologous groups of genes suitable for phylogenomics (i.e., one-to-one orthologs and SNAP-OGs) are subsequently aligned and trimmed. (D) The resulting multiple sequence alignments can be (left) concatenated into a supermatrix or (right) collections of single-locus phylogenies can be used in a coalescence-based approach. (E) Support for the inferred phylogeny can be evaluated using multiple approaches, such as bootstrap statistics, gene support frequencies / concordance factors, and phylogenomic subsampling. (F) Divergence time estimates can be inferred using node dating, tip dating, or fossil-free analyses. Branch lengths represent substitutions per site in the phylogeny on the left and time on the right. Grey boxes in the timetree represent confidence intervals. Silhouette images were obtained from PhyloPic (https://www.phylopic.org/); credit goes to their respective contributors.
Figure 1. A workflow for phylogenomic inference. (A) The first step in a phylogenomic workflow is data acquisition and preparation. This often entails identifying gene boundaries in genome assemblies or assembling transcripts in transcriptomes. (B) The next step is to identify orthologs using (left) de novo approaches—for example, all-by-all sequence similarity calculations followed by graph-based clustering—or (right) from predetermined sets of orthologs. (C) Orthologous groups of genes suitable for phylogenomics (i.e., one-to-one orthologs and SNAP-OGs) are subsequently aligned and trimmed. (D) The resulting multiple sequence alignments can be (left) concatenated into a supermatrix or (right) collections of single-locus phylogenies can be used in a coalescence-based approach. (E) Support for the inferred phylogeny can be evaluated using multiple approaches, such as bootstrap statistics, gene support frequencies / concordance factors, and phylogenomic subsampling. (F) Divergence time estimates can be inferred using node dating, tip dating, or fossil-free analyses. Branch lengths represent substitutions per site in the phylogeny on the left and time on the right. Grey boxes in the timetree represent confidence intervals. Silhouette images were obtained from PhyloPic (https://www.phylopic.org/); credit goes to their respective contributors.
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Figure 2. The ABBA-BABA test for detecting introgression/hybridization. To detect introgression/hybridization in the (A) four-taxon case (represented as T1 through T4 where T4 is the outgroup taxa), the D-statistic or ABBA-BABA test can be used. (B) The orange dot represents a mutation from the ancestral allele ‘A’ (blue) to a derived allele ‘B’ (orange). The BBAA pattern, which is not directly accounted for in the ABBA-BABA test, is a biallelic site that follows the organismal phylogeny. Asymmetric patterns of ABBA and BABA biallelic site patterns suggest the occurrence of an introgression/hybridization event. The ABBA pattern can arise from (C) incomplete lineage sorting or (D) introgression/hybridization, whereas the (E) BABA pattern can arise from incomplete lineage sorting; thus, unequal frequencies of ABBA and BABA patterns are suggestive of introgression/hybridization.
Figure 2. The ABBA-BABA test for detecting introgression/hybridization. To detect introgression/hybridization in the (A) four-taxon case (represented as T1 through T4 where T4 is the outgroup taxa), the D-statistic or ABBA-BABA test can be used. (B) The orange dot represents a mutation from the ancestral allele ‘A’ (blue) to a derived allele ‘B’ (orange). The BBAA pattern, which is not directly accounted for in the ABBA-BABA test, is a biallelic site that follows the organismal phylogeny. Asymmetric patterns of ABBA and BABA biallelic site patterns suggest the occurrence of an introgression/hybridization event. The ABBA pattern can arise from (C) incomplete lineage sorting or (D) introgression/hybridization, whereas the (E) BABA pattern can arise from incomplete lineage sorting; thus, unequal frequencies of ABBA and BABA patterns are suggestive of introgression/hybridization.
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Figure 3. Phylogenetic signatures of a horizontal gene transfer event. To detect horizontal gene transfer, (A) organismal histories are compared to (B) single-locus phylogenies. Horizontal gene transfer is suggested when sequences are placed within an unexpected taxonomic group in single-locus phylogenies. Here, an example of prokaryote-to-eukaryote transfer is depicted wherein an organism from the fungal kingdom (orange) is monophyletic with prokaryotic sequences (blue). The horizontal gene transfer event is depicted as a grey arrow. Silhouette images were obtained from PhyloPic (https://www.phylopic.org/); credit goes to their respective contributors.
Figure 3. Phylogenetic signatures of a horizontal gene transfer event. To detect horizontal gene transfer, (A) organismal histories are compared to (B) single-locus phylogenies. Horizontal gene transfer is suggested when sequences are placed within an unexpected taxonomic group in single-locus phylogenies. Here, an example of prokaryote-to-eukaryote transfer is depicted wherein an organism from the fungal kingdom (orange) is monophyletic with prokaryotic sequences (blue). The horizontal gene transfer event is depicted as a grey arrow. Silhouette images were obtained from PhyloPic (https://www.phylopic.org/); credit goes to their respective contributors.
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